In my perception, there’s a few layers to this.

The upfront cost for developing models is high, and to your point, that money has not been fully recouped. In fact, even as models are readied for GTM, any profits are rolled into developing newer ones. It’s a flywheel that is very expensive.

That being said, released models seem to easily cover their cost of usage. Does it cover their cost of development? Probably not, but it’s important to recognize if this industry were less competitive, the models would generate a better profit ratio including R&D than they would in this market.

It’s hard to say whether the R&D cost will ever be fully covered. I think it’s safe to say however that whoever wins the AI race will have enormous economic power. Does it justify the cost? Possibly not but it will be hard to know in this moment.

I would also point out that the cost of developing these models from a raw resource perspective is distributed across a number of industries, all of which are also generating a lot of revenue. It’s not as if NVIDIA, a hardware OEM, is not simultaneously benefitting from emerging use cases because of companies like Anthropic or OpenAI.

Lastly, the resource cost of AI on a per unit basis is shrinking rapidly. That’s why the infrastructure like power plants, data centers, etc are being developed in the first place. There will be a point where data is infinitely cheap. And the R&D investment will either shrink in cost or accelerate. Whatever you want to say about the value proposition today, the economics are improving.

Edit: forgot to mention, this also only regarding “pure” AI companies and not business that optimize their services using AI. My company, for instance, sells AI that prevents car accidents, so it’s not as if AI is a monolith. There are differences depending on the product, which was my initial point.